The reconstruction of accurate three-dimensional models from images has been an area of computer vision research that has experienced significant advances in the last several years. For example, several three-dimensional reconstruction methods have been disclosed that are superior to the use of basic laser ranging systems and are much cheaper to implement.
The photorealistic reconstructions yielded by such three-dimensional modeling techniques have enhanced a large number of applications in many areas, ranging from architecture to visual effects. Such methods can also complement photorealistic accuracy with scalability, enabling large-scale reconstructions for a number of applications.
However, notwithstanding this marked progress, most of the recent work in the image reconstruction field has focused on outdoor scenes. Therefore, recovering an accurate three-dimensional model of an indoor location remains a challenging problem.
For example, indoor scenes exhibit severe illumination variability, large untextured areas, and wide baselines. Therefore, point matching and camera calibration become easily prone to errors, thus leading to poor reconstructions.
Floorplan reconstructions, in particular, exhibit additional difficulties. For example, indoor environments often contain clutter, such as lamps, desks, chairs, etc., that serve to hide the architectural lines of the room. Furthermore, reconstructing a floorplan from datasets that include large degrees of variability and ambiguity (i.e. noisy data), which is the result of most common data capturing methods, is an inherently difficult problem.
In particular, the floorplans resulting from certain methods may not reflect the relative simplicity of a floorplan when confronted with noisy data. For example, certain methods can overfit a given dataset and can result in a floorplan that is overly complex and contains many deviations from a typical rectilinear floorplan. In addition, extraneous items, such as data reflecting items captured through windows or glass doors, can be captured in the floorplan. Such deviations are often incorrect, unwanted, and cause additional difficulty when using a three-dimensional model based on the determined floorplan by, for example, stitching or texture mapping panoramic images according to the floorplan.
Other methods can confront the problem of floorplan reconstruction by treating it as a volumetric segmentation procedure in which each voxel is categorized as either indoor or outdoor. However, such methods often exhibit shrinkage bias. Therefore, thin indoor walls that separate contiguous rooms may fail to be recovered. Further, regularization terms often lead to problems including difficulty with local minima, thus rendering the methods sensitive to initialization problems.
Therefore, improved systems and methods for floorplan reconstruction and visualization are desirable.